Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
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DOI：
10.1109/tvcg.2019.2934594
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发表时间：
2019-07
影响因子：
5.2
通讯作者：
Shusen Liu;Di Wang;D. Maljovec;Rushil Anirudh;Jayaraman J. Thiagarajan;S. A. Jacobs;B. V. Essen;D. Hysom;Jae-Seung Yeom;J. Gaffney;L. Peterson;Peter B. Robinson;H. Bhatia;Valerio Pascucci;B. Spears;P. Bremer
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文献类型：
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作者：
Shusen Liu;Di Wang;D. Maljovec;Rushil Anirudh;Jayaraman J. Thiagarajan;S. A. Jacobs;B. V. Essen;D. Hysom;Jae-Seung Yeom;J. Gaffney;L. Peterson;Peter B. Robinson;H. Bhatia;Valerio Pascucci;B. Spears;P. Bremer
随着机器学习技术在科学和工程领域的大规模应用的迅速采用，可视化领域的两大挑战也随之而来。首先，黑盒模型的利用（例如，深度神经网络）需要探索和解释模型行为的先进技术。其次，计算的快速发展产生了巨大的数据集，需要能够处理数百万或更多样本的技术。尽管已经提出了一些解决这些可解释性挑战的方案，但它们通常不会超过数千个样本，也不会提供科学家正在寻找的高级直觉。在这里，我们提出了第一个可扩展的解决方案，以探索和分析科学数据分析管道中经常遇到的高维函数。通过结合一个新的流邻域图的建设，相应的拓扑计算，和一种新的数据聚合方案，即拓扑感知数据立方体，我们使互动式探索的拓扑和几何方面的高维数据。在高能量密度（HED）物理学和计算生物学的两个用例之后，我们展示了这些功能如何在这两个应用中产生关键的新见解。
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpretability challenges have been proposed, they typically do not scale beyond thousands of samples, nor do they provide the high-level intuition scientists are looking for. Here, we present the first scalable solution to explore and analyze high-dimensional functions often encountered in the scientific data analysis pipeline. By combining a new streaming neighborhood graph construction, the corresponding topology computation, and a novel data aggregation scheme, namely topology aware datacubes, we enable interactive exploration of both the topological and the geometric aspect of high-dimensional data. Following two use cases from high-energy-density (HED) physics and computational biology, we demonstrate how these capabilities have led to crucial new insights in both applications.